
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )





class     __UpperCamelCase :






     """simple docstring"""







     def __init__(				self							:							Optional[int]     ,						_A							:							List[str]     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		parent
       __SCREAMING_SNAKE_CASE      :	int					=		13
       __SCREAMING_SNAKE_CASE      :	Any					=		7
       __SCREAMING_SNAKE_CASE      :	List[str]					=		30
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.seq_length + self.mem_len
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		15
       __SCREAMING_SNAKE_CASE      :	Any					=		True
       __SCREAMING_SNAKE_CASE      :	Dict					=		True
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		99
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[10, 50, 80]
       __SCREAMING_SNAKE_CASE      :	int					=		32
       __SCREAMING_SNAKE_CASE      :	int					=		32
       __SCREAMING_SNAKE_CASE      :	Dict					=		4
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		8
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		128
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		2
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		2
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		None
       __SCREAMING_SNAKE_CASE      :	Dict					=		1
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		0
       __SCREAMING_SNAKE_CASE      :	int					=		3
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.vocab_size - 1
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		0.01



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		None
       if self.use_labels:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		TransfoXLConfig(
           vocab_size=self.vocab_size     ,						mem_len=self.mem_len     ,						clamp_len=self.clamp_len     ,						cutoffs=self.cutoffs     ,						d_model=self.hidden_size     ,						d_embed=self.d_embed     ,						n_head=self.num_attention_heads     ,						d_head=self.d_head     ,						d_inner=self.d_inner     ,						div_val=self.div_val     ,						n_layer=self.num_hidden_layers     ,						eos_token_id=self.eos_token_id     ,						pad_token_id=self.vocab_size - 1     ,						init_range=self.init_range     ,						num_labels=self.num_labels     ,						)

       return (config, input_ids_a, input_ids_a, lm_labels)



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       random.seed(self.seed )
       tf.random.set_seed(self.seed )



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							Dict     ,						_A							:							str     ,						_A							:							Any     ,						_A							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		TFTransfoXLModel(_A )

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		model(_A ).to_tuple()

       __SCREAMING_SNAKE_CASE      :	Tuple					=		{'''input_ids''': input_ids_a, '''mems''': mems_a}

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model(_A ).to_tuple()

       self.parent.assertEqual(hidden_states_a.shape     ,						(self.batch_size, self.seq_length, self.hidden_size) )
       self.parent.assertEqual(hidden_states_a.shape     ,						(self.batch_size, self.seq_length, self.hidden_size) )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]     ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,						)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]     ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,						)



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							int     ,						_A							:							List[str]     ,						_A							:							int     ,						_A							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		TFTransfoXLLMHeadModel(_A )

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model(_A ).to_tuple()

       __SCREAMING_SNAKE_CASE      :	Any					=		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		model(_A ).to_tuple()

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		model([input_ids_a, mems_a] ).to_tuple()

       __SCREAMING_SNAKE_CASE      :	int					=		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		model(_A ).to_tuple()

       self.parent.assertEqual(lm_logits_a.shape     ,						(self.batch_size, self.seq_length, self.vocab_size) )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]     ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,						)

       self.parent.assertEqual(lm_logits_a.shape     ,						(self.batch_size, self.seq_length, self.vocab_size) )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]     ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,						)



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							Tuple     ,						_A							:							int     ,						_A							:							Dict     ,						_A							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		TFTransfoXLForSequenceClassification(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		model(_A )
       self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels) )




     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.prepare_config_and_inputs()
       ((__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE),		(__SCREAMING_SNAKE_CASE))      :	Optional[Any]					=		config_and_inputs
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{'''input_ids''': input_ids_a}
       return config, inputs_dict






@require_tf
class     __UpperCamelCase (							lowerCAmelCase__ ,						lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     lowerCAmelCase_							   =			() if is_tf_available() else ()
     lowerCAmelCase_							   =			(
         {
             '''feature-extraction''': TFTransfoXLModel,
             '''text-classification''': TFTransfoXLForSequenceClassification,
             '''text-generation''': TFTransfoXLLMHeadModel,
             '''zero-shot''': TFTransfoXLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
     lowerCAmelCase_							   =			False
     lowerCAmelCase_							   =			False
     lowerCAmelCase_							   =			False
     lowerCAmelCase_							   =			False



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							Union[str, Any]     ,						_A							:							List[Any]     ,						_A							:							Optional[Any]     ,						_A							:							Optional[Any]     ,						_A							:							Union[str, Any] ):







       """simple docstring"""




       if pipeline_test_casse_name == "TextGenerationPipelineTests":
         # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
         # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
         # tokenizer.
         return True

       return False



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		TFTransfoXLModelTester(self )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		ConfigTester(self     ,						config_class=_A     ,						d_embed=37 )



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       self.config_tester.run_common_tests()



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       self.model_tester.set_seed()
       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*_A )



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       self.model_tester.set_seed()
       __SCREAMING_SNAKE_CASE      :	str					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*_A )



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_A )



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[TFTransfoXLForSequenceClassification]

       for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE      :	Dict					=		model_class(_A )
         assert isinstance(model.get_input_embeddings()     ,						tf.keras.layers.Layer )
         if model_class in list_other_models_with_output_ebd:
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		model.get_output_embeddings()
           assert isinstance(_A     ,						tf.keras.layers.Layer )
           __SCREAMING_SNAKE_CASE      :	Any					=		model.get_bias()
           assert name is None
         else:
           __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model.get_output_embeddings()
           assert x is None
           __SCREAMING_SNAKE_CASE      :	Dict					=		model.get_bias()
           assert name is None



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       pass



     @slow
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		TFTransfoXLModel.from_pretrained(_A )
         self.assertIsNotNone(_A )




     @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       pass






@require_tf
class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     @unittest.skip('''Skip test until #12651 is resolved.''' )
     @slow
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
       # fmt: off
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]     ,						dtype=tf.intaa )  # noqa: E231
       # fmt: on
       #  In 1991 , the remains of Russian Tsar Nicholas II and his family
       #  ( except for Alexei and Maria ) are discovered .
       #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
       #  remainder of the story . 1883 Western Siberia ,
       #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
       #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
       #  father initially slaps him for making such an accusation , Rasputin watches as the
       #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
       #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
       #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

       # fmt: off
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
       # fmt: on
       #  In 1991, the remains of Russian Tsar Nicholas II and his family (
       #  except for Alexei and Maria ) are discovered. The voice of young son,
       #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
       #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
       #  and a group of men to perform magic. Rasputin has a vision and
       #  denounces one of the men as a horse thief. Although his father initially
       #  slaps him for making such an accusation, Rasputin watches as the man
       #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
       #  of the Virgin Mary, prompting him to become a priest.
       #  Rasputin quickly becomes famous, with people, even a bishop, begging for
       #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
       # Nicholas II and his family were discovered. The voice of <unk> young son,
       # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		model.generate(_A     ,						max_length=200     ,						do_sample=_A )
       self.assertListEqual(output_ids[0].numpy().tolist()     ,						_A )








'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint


@require_torch
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[str]:
								# For consistency across different places the DisjunctiveConstraint is called,
								# dc.token_ids is a list of integers. It is also initialized only by integers.

								UpperCAmelCase_      :       Union[str, Any]          =       [[1, 2, 4], [1, 2, 3, 4]]
								UpperCAmelCase_      :       Dict          =       DisjunctiveConstraint(_A      )
								self.assertTrue(isinstance(dc.token_ids				, _A      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]      ), torch.LongTensor([1, 2, 3, 4, 5]      )]      )




					def 		A      (  self  :		Optional[int]      )    ->							List[Any]:
								# We can't have constraints that are complete subsets of another. This leads to a preverse
								# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
								# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
								# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
								# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
								UpperCAmelCase_      :       Optional[Any]          =       [[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(_A      )  # fails here




					def 		A      (  self  :		List[str]      )    ->							int:
								UpperCAmelCase_      :       Dict          =       [[1, 2, 3], [1, 2, 4]]

								UpperCAmelCase_      :       List[Any]          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								UpperCAmelCase_      :       int          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(2      )
								UpperCAmelCase_      :       Optional[int]          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(3      )
								UpperCAmelCase_      :       List[Any]          =       stepped is True and completed is True and reset is False
								self.assertTrue(_A      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3]      )





					def 		A      (  self  :		List[Any]      )    ->							Any:
								UpperCAmelCase_      :       int          =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								UpperCAmelCase_      :       str          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(4      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2, 4]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5]      )

								dc.reset()

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 3      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 2      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.remaining() == 0      )
								self.assertTrue(dc.current_seq == [1, 2, 5]      )


